@inproceedings{de-la-garza-etal-2026-extracting,
    title = "Extracting Signs from Weakly Aligned Sign Language Corpora: A Study on {LSF} and {LSM}",
    author = "de la Garza, Lorena  and
      Halbout, Julie  and
      Lascar, Julie  and
      Martinez, Niels  and
      Curiel, Arturo  and
      Gouiff{\`e}s, Mich{\`e}le  and
      Braffort, Annelies",
    editor = "Efthimiou, Eleni  and
      Fotinea, Stavroula-Evita  and
      Hanke, Thomas  and
      Hochgesang, Julie A.  and
      Mesch, Johanna  and
      Schulder, Marc",
    booktitle = "Proceedings of the {LREC} 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/test-year-match/2026.signlang-1.19/",
    doi = "10.63317/38kfot52b4dz",
    pages = "174--183",
    abstract = "This paper presents a framework for the automatic annotation of sign language data across different recording conditions, including original and interpreted content. The proposed approach integrates weak alignment, sign segmentation, and multiple instance learning with a contrastive loss. The resulting annotations are subsequently refined and filtered to enhance their reliability. Our method was applied to two historically related sign languages, French Sign Language (LSF) and Mexican Sign Language (LSM). This led to the creation of two signaries, comprising approximately 2k categories in LSF (25k occurrences) and 41 categories in LSM (1k occurrences). Both resources provide valuable support for future research in artificial intelligence and linguistics, particularly for comparative analyses between the two languages. A seminal analysis is presented as part of this paper."
}